Spatio-temporal modeling for residential burglary

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Spatio-temporal modeling is widely recognized as a promising means for predicting crime patterns. Despite their enormous potential, the available methods are still in their infancy. A lot of research focuses on crime hotspot detection and geographic crime clusters, while a systematic approach to include the temporal component of the underlying crime distributions is still under-researched. In this paper, we gain further insight in predictive crime modeling by including a spatio-temporal interaction component in the prediction of residential burglaries. Based on an extensive dataset, we show that including additive space-time interactions leads to significantly better predictions.
Original languageEnglish
Title of host publicationDATA ANALYTICS 2017 : The Sixth International Conference on Data Analytics
Subtitle of host publication[Proceedings]
EditorsSandjai Bhulai, Dimitris Kardaras
Number of pages6
ISBN (Print)9781612086033
Publication statusPublished - 2017


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